Tourist Arrivals Demand Forecasting Using Rough Set-Based Time Series Models

This paper uses different univariate and multivariate autoregression and soft computing models for forecasting qualitative time series data. Rough set theory (RST) is one of the most convenient soft computing methods to investigate the imprecision and ambiguity of an information table by using qualitative dependent and independent variables. Moreover, applications based on rough set theory are used for the classification problem. The rough set is quite different from the other statistical and machine learning data analysis approaches based on mathematical equations. The empirical analysis indicates that the rough set is a highly accurate soft computing model for forecasting FTA data.